Data science projects can stall for many reasons, but Eugene Yan argues that a strong strategy alone is not enough. In his post on delivering successful data science projects, he makes the case that execution is just as important as the plan itself. The gap between what a team intends to build and what it actually ships is closed through deliberate, ongoing work during the project.
Yan's advice centers on the practices that happen after the roadmap is set: staying aligned with business needs, checking assumptions, and maintaining a focus on delivery. The piece is a reminder that project success is often determined by discipline and follow-through rather than by the cleverness of the model. Since this is the only source in this brief, there are no differing viewpoints to compare; the analysis follows directly from Yan's argument.